Determination of Trace Elements in Edible Nuts in the Beijing Market by ICP-MS

Nuts have received increased attention from the public in recent years as important sources of some essential elements, and information on the levels of elements in edible nuts is useful to consumers. Determination of the elemental distributions in nuts is not only necessary in evaluating the total dietary intake of the essential elements, but also useful in detecting heavy metal contamination in food. The aim of this study was to determine the mineral contents in edible nuts, and to assess the food safety of nuts in the Beijing market. Levels of Li, Cr, Mn, Co, Cu, Zn, As, Se, Rb, Sr, Mo, Cd, Cs, Ba, Pb, Th, and U in 11 types of edible nuts and seeds （macadamia nuts, lotus nuts, pistachios, sunflower seeds, pine nuts, almonds, walnuts, chestnuts, hazelnuts, cashews, and ginkgo nuts） as well as raisins were determined by inductively coupled plasma mass spectrometry （ICP-MS）. The accuracy of the method was validated using standard reference materials GBWlO014 （cabbage） and GBWlO016 （tea）. Our results provide useful information for evaluating the levels of trace elements in edible nuts in the Beijing market, will be helpful for improving food safety, and will aid in better protecting consumer interests.

Generative adversarial networks for detecting contamination events in water distribution systems using multi-parameter,multi-site water quality monitoring

Contamination events in water distribution networks(WDNs)can have a huge impact on water supply and public health;increasingly,online water quality sensors are deployed for real-time detection of contamination events.Machine learning has been used to integrate multivariate time series water quality data at multiple stations for contamination detection;however,accurate extraction of spatial features in water quality signals remains challenging.This study proposed a contamination detection method based on generative adversarial networks(GANs).The GAN model was constructed to simultaneously consider the spatial correlation between sensor locations and temporal information of water quality indicators.The model consists of two networksda generator and a discriminatordthe outputs of which are used to measure the degree of abnormality of water quality data at each time step,referred to as the anomaly score.Bayesian sequential analysis is used to update the likelihood of event occurrence based on the anomaly scores.Alarms are then generated from the fusion of single-site and multi-site models.The proposed method was tested on a WDN for various contamination events with different characteristics.Results showed high detection performance by the proposed GAN method compared with the minimum volume ellipsoid benchmark method for various contamination amplitudes.Additionally,the GAN method achieved high accuracy for various contamination events with different amplitudes and numbers of anomalous water quality parameters,and water quality data from different sensor stations,highlighting its robustness and potential for practical application to real-time contamination events.

Fe_(2)O_(3)-decorated boron/nitrogen-co-doped carbon nanosheets as an electrochemical sensing platform for ultrasensitive determination of paraquat in natural water

Boron/nitrogen-co-doped carbon(BCN)nanosheets decorated with Fe_(2)O_(3) nanocrystals(Fe_(2)O_(3)–BCN)were cast on a glassy carbon electrode(GCE)and applied as an electrochemical sensor to effectively detect paraquat(PQ),a toxic herbicide,in aqueous environments.A linear experiment performed using square wave voltammetry(SWV)under optimized experimental conditions produced a decent linear relationship and a low detection limit(LOD)of 2.74 nmol/L(S/N=3).Repeatability,reproducibility,stability,and interference experiments confirmed that the Fe_(2)O_(3)–BCN/GCE system exhibited decent electrochemical sensing performance for PQ molecules.Notably,the designed sensor showed high selectivity and a decent linear relationship with PQ concentration in natural water samples.To the best of our knowledge,this is the first study on the preparation of Fe_(2)O_(3)–BCN nanosheets for PQ detection.The proposed sensor can be employed as an effective alternative tool for distinguishing and processing PQ.